Tracking user physical activity with multiple devices

ABSTRACT

Methods, devices, and computer programs are presented for creating a unified data stream from multiple data streams acquired from multiple devices. One method includes an operation for receiving activity data streams from the devices, each activity data stream being associated with physical activity data of a user. Further, the method includes an operation for assembling the unified activity data stream for a period of time. The unified activity data stream includes data segments from the data streams of at least two devices, and the data segments are organized time-wise over the period of time.

CLAIM OF PRIORITY

This application is a Continuation application under 35 USC §120 of U.S.application Ser. No. 14/174,497, entitled “Method of Data Synthesis”,and filed on Feb. 6, 2014, which claims priority from U.S. ProvisionalPatent Application No. 61/762,210, filed Feb. 7, 2013, and entitled“Method of Data Synthesis,” all of which are herein incorporated byreference.

This application is a continuation-in-part of U.S. patent applicationSer. No. 13/959,714, filed on Aug. 5, 2013, titled “Methods and Systemsfor Identification of Event Data Having Combined Activity and LocationInformation of Portable Monitoring Devices”, which is acontinuation-in-part of U.S. patent application Ser. No. 13/693,334 (nowissued as U.S. Pat. No. 8,548,770, issued on Oct. 1, 2013), filed onDec. 4, 2012, titled “Portable Monitoring Devices and Methods forOperating Same”, which is a divisional of U.S. patent application Ser.No. 13/667,229 (now issued as U.S. Pat. No. 8,437,980, issued on May 7,2013), filed on Nov. 2, 2012, titled “Portable Monitoring Devices andMethods for Operating Same”, which is a divisional of U.S. patentapplication Ser. No. 13/469,027, now U.S. Pat. No. 8,311,769, filed onMay 10, 2012, titled “Portable Monitoring Devices and Methods forOperating Same”, which is a divisional of U.S. patent application Ser.No. 13/246,843, now U.S. Pat. No. 8,180,591, filed on Sep. 27, 2011,which is a divisional of U.S. patent application Ser. No. 13/156,304,filed on Jun. 8, 2011, titled “Portable Monitoring Devices and Methodsfor Operating Same”, which claims the benefit of and priority to, under35 U.S.C. 119§(e), to U.S. Provisional Patent Application No.61/388,595, filed on Sep. 30, 2010, and titled “Portable MonitoringDevices and Methods for Operating Same”, and to U.S. Provisional PatentApplication No. 61/390,811, filed on Oct. 7, 2010, and titled “PortableMonitoring Devices and Methods for Operating Same”, all of which arehereby incorporated by reference in their entirety.

This application is a continuation-in-part of Ser. No. 13/959,714, filedAug. 5, 2013, titled “Methods and Systems for Identification of EventData Having Combined Activity and Location Information of PortableMonitoring Devices”, which claims priority to U.S. Provisional PatentApplication 61/680,230 filed Aug. 6, 2012, and is a continuation-in-partof U.S. patent application Ser. No. 13/759,485 (now issued as U.S. Pat.No. 8,543,351, issued on Sep. 24, 2013), filed on Feb. 5, 2013, titled“Portable Monitoring Devices and Methods for Operating Same”, which is adivisional of U.S. patent application Ser. No. 13/667,229, filed on Nov.2, 2012, titled “Portable Monitoring Devices and Methods for OperatingSame” (now issued as U.S. Pat. No. 8,543,351, issued on May 7, 2013),which is a divisional of U.S. patent application Ser. No. 13/469,027,now U.S. Pat. No. 8,311,769, filed on May 10, 2012, titled “PortableMonitoring Devices and Methods for Operating Same”, which is adivisional of U.S. patent application Ser. No. 13/246,843, now U.S. Pat.No. 8,180,591, filed on Sep. 27, 2011, which is a divisional of U.S.patent application Ser. No. 13/156,304, filed on Jun. 8, 2011, titled“Portable Monitoring Devices and Methods for Operating Same”, whichclaims the benefit of and priority to, under 35 U.S.C. 119§(e), to U.S.Provisional Patent Application No. 61/388,595, filed on Sep. 30, 2010,and titled “Portable Monitoring Devices and Methods for Operating Same”and to U.S. Provisional Patent Application No. 61/390,811, filed on Oct.7, 2010, and titled “Portable Monitoring Devices and Methods forOperating Same”, all of which are hereby incorporated by reference intheir entirety.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related by subject matter to U.S. patent applicationSer. No. ______ (Attorney Docket No. FITBP010AC1) filed on the same dayas the instant application and entitled “Method of Data Synthesis”,which is incorporated herein by reference.

BACKGROUND

1. Field of the Invention

The present embodiments relate to methods, devices, systems, andcomputer programs for analyzing data, and more particularly, methods,devices, systems, and computer programs for consolidating overlappingdata provided by multiple devices.

2. Description of the Related Art

The use of portable devices has grown exponentially over the last fewdecades, and in particularly, the use of biometric monitoring devicesthat users wear on their bodies to measure activity levels, as wellmeasuring environmental or user parameters (e.g., temperature, heartrate, altitude, etc.). Sometimes, data related to user activities may beacquired from multiple devices, such as a pedometer, a smart phone, aGPS (Global Positioning System) device, a thermometer, a weight scale,etc.

Occasionally, two or more devices can provide information about the sameparameter related to activities of the user. For example, a user may geta step count from a pedometer worn on the wrist and from a step counterembedded on a running shoe. However, the accuracy of the differentdevices may change according to the inherent accuracy of the device oraccording to environmental circumstances (e.g., poor GPS satellite datareception).

When the user gets conflicting information from multiple devices, theuser may not know which is the accurate data. A system is desired toidentify the best source of information in order to provide the bestpossible data to the user.

It is in this context that embodiments arise.

SUMMARY

Methods, devices, systems, and computer programs are presented forconsolidating overlapping data provided by multiple sources. It shouldbe appreciated that the present embodiments can be implemented innumerous ways, such as a method, an apparatus, a system, a device, or acomputer program on a computer readable medium. Several embodiments aredescribed below.

The present embodiments relate to methods, systems, and computerprograms for combining or synthesizing multiple data streams into aunified data stream while maintaining accuracy and integrity of thedata. In an embodiment, the data includes environmental and biometricdata. In another embodiment, the multiple data streams originate from,or are collected by, multiple devices that monitor the same or similarphysical phenomena. For example, one embodiment includes a method usedby a cloud-based activity-monitoring server to combine data streams frommultiple portable biometric monitoring devices tracking a single user orgroup of users. In another embodiment, the method pertains to a device(e.g., personal computer, tablet computer, smartphone) that combinesdata streams from multiple portable biometric monitoring devicestracking a single user or a group of users.

In one embodiment, a method includes operations for receiving aplurality of activity data streams from a plurality of devices, eachactivity data stream being associated with physical activity data of auser. In addition, the method includes an operation for assembling aunified activity data stream for the user over a period of time, theunified activity data stream including data segments from the datastreams of at least two devices of the plurality of devices, and thedata segments being organized time-wise over the period of time. In oneembodiment, the operations of the method are executed by a processor.

In another embodiment, a server includes a communications module, amemory, and a processor. The communications module is operable toreceive a plurality of activity data streams from a plurality ofdevices, each activity data stream being associated with physicalactivity data of a user. Further, the memory is operable to store theplurality of activity data streams and a unified activity data streamthat includes data segments from the data streams of at least twodevices of the plurality of devices. In addition, the processor isoperable to assemble the unified activity data stream for the user overa period of time. The data segments are organized time-wise over theperiod of time.

In another embodiment, a non-transitory computer-readable storage mediumstoring a computer program is provided. The computer-readable storagemedium includes program instructions for receiving a plurality ofactivity data streams from a plurality of devices, each activity datastream being associated with physical activity data of a user. Further,the storage medium includes program instructions for assembling aunified activity data stream for the user over a period of time, theunified activity data stream including data segments from the datastreams of at least two devices of the plurality of devices, the datasegments being organized time-wise over the period of time.

In yet another embodiment, one method includes operations for receivingdata streams regarding activity of a user, each data stream beingassociated with a respective device of a plurality of devices, and forevaluating one or more rules for consolidating the data streams into aunified activity data stream. Evaluating the one or more rules furtherincludes identifying first time segments where data is available from asingle device from the plurality of devices; adding the available datato the unified activity data stream for the first time segments;identifying second time segments where data exists from two or moredevices from the plurality of devices; and adding data to the secondtime segments based on the existing data from the two or more devicesand based on the one or more rules. Further, the method includes anoperation for storing the unified activity data stream for presentationto the user. In one embodiment, the operations of the method areexecuted by a processor.

Other aspects will become apparent from the following detaileddescription, taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings.

FIG. 1 is a block diagram of a system architecture according to oneembodiment.

FIG. 2 is a block diagram of a data synthesizer according to oneembodiment.

FIG. 3 is a block diagram of a data parser according to one embodiment.

FIG. 4 is a block diagram of an input specific parser library accordingto one embodiment.

FIG. 5 is a block diagram of a standard data format parser according toone embodiment.

FIGS. 6-8 are diagrams of user interface screens according to severalembodiments.

FIG. 9 is a diagram illustrating data that could be collected during aperson's daily routine.

FIG. 10 is a block diagram of data stream synthesis according to oneembodiment.

FIG. 11A is a block diagram of data stream synthesis according to oneembodiment.

FIG. 11B illustrates an exemplary architecture of the data synthesizer,according to one embodiment.

FIG. 11C is a flowchart of a method for combining multiple data streams,according to one embodiment.

FIG. 11D is an exemplary rules database according to one embodiment.

FIG. 11E illustrates the selection of different inputs, based oncombination rules, to generate a compound output, according to oneembodiment.

FIG. 12 is a diagram of data stream synthesis from multiple devicesaccording to one embodiment.

FIGS. 13, 15, and 16 illustrate user interface screens for devicemanagement according to several embodiments.

FIG. 14 is a user interface screen with a targeted advertisementaccording to one embodiment.

FIG. 17 is a diagram of a user account website page example in which adevice is used for measuring or estimating the number of floors climbed,according to one embodiment.

FIG. 18 is a diagram of a website page for a device which cannot trackthe number of floors climbed, according to one embodiment.

FIG. 19 is a simplified schematic diagram of a device for implementingembodiments described herein.

FIG. 20 is a flowchart illustrating an algorithm for performing datasynthesis for data provided by a plurality of devices, according to oneembodiment.

FIG. 21 is a flowchart illustrating an algorithm for creating a unifieddata stream from a plurality of data streams acquired from a pluralityof devices, according to one embodiment.

FIG. 22 illustrates an example where various types of activities ofusers can be captured or collected by activity tracking devices, inaccordance with various embodiments of the present invention.

DETAILED DESCRIPTION

Methods, devices, systems, and computer programs are presented forconsolidating overlapping data provided by multiple devices. It will beapparent, that the present embodiments may be practiced without some orall of these specific details. In other instances, well-known processoperations have not been described in detail in order not tounnecessarily obscure the present embodiments.

FIG. 1 is a block diagram of a system architecture according to oneembodiment. Portable biometric devices will be referred to herein by wayof example to illustrate aspects of the invention. The embodiments arenot limited to portable biometric devices, however, as the datasynthesis of multiple data sources may be applied to any type of data.

Some biometric monitoring devices are portable and have shapes and sizesthat are adapted to couple to the body of a user (e.g., a pedometers102, 106), while other devices are carried by the user (e.g., mobilephone 108, laptop 110, tablet), and other devices may be stationary(e.g., electronic scale 104, a digital thermometer, personal computer).

The devices collect one or more types of physiological or environmentaldata from embedded sensors or external devices. The devices can thencommunicate the data to other devices, to one or more servers 112, or toother internet-viewable sources. As one example, while the user iswearing a biometric monitoring device 102, the device can calculate andstore the number of steps taken by the user (the user's step count) fromdata collected by embedded sensors. Data representing the user's stepcount is then transmitted to an account on a web service (such aswww.fitbit.com, for example) where the data may be stored, processed,and viewed by the user. Indeed, the device may measure or calculate aplurality of other physiological metrics in addition to, or in place of,the user's step count.

These metrics include, but are not limited to, energy expenditure (e.g.,calorie burn), floors climbed or descended, heart rate, heart ratevariability, heart rate recovery, location and/or heading (e.g., throughGPS), elevation, ambulatory speed and/or distance traveled, swimming lapcount, bicycle distance and/or speed, blood pressure, blood glucose,skin conduction, skin and/or body temperature, electromyography,electroencephalography, weight, body fat, caloric intake, nutritionalintake from food, medication intake, sleep periods (i.e., clock time),sleep phases, sleep quality, and/or sleep duration, and respirationrate. The device may also measure or calculate metrics related to theenvironment around the user such as barometric pressure, weatherconditions (e.g., temperature, humidity, pollen count, air quality,rain/snow conditions, wind speed), light exposure (e.g., ambient light,UV light exposure, time and/or duration spent in darkness), noiseexposure, radiation exposure, and magnetic field.

As used herein, the term “sync” refers to the action of exchanging databetween a first device and a second device to update the second devicewith new information available to the first device that is not yetavailable to the second device. Additionally, “sync” may also referredto the exchange of information between two devices to provide updates toone of the devices with information available to the other device, or tocoordinate information that is available, overlapping, or redundant inboth devices. “Sync” may also be used in reference to sending and/orreceiving data to and/or from another computing device or electronicstorage devices including, but not limited to, a personal computer, acloud based server, and a database. In some embodiments, a sync from oneelectronic device to another may occur through the use of one or moreintermediary electronic devices. For example, data from a personalbiometric device may be transmitted to a smart phone that forwards thedata to a server.

Furthermore, a device or system combining multiple data streams maycalculate metrics derived from this data. For example, the device orsystem can calculate the user's stress levels and the risk level ofelevated stress, through one or more of heart rate variability, skinconduction, noise pollution, and sleep quality. Conversely, the deviceor system may calculate the user's stress reduction (e.g., calmness), orthe stress reduction risk through one or more of the precedingparameters. In another embodiment, the device or system may determinethe efficacy of a medical intervention (e.g., medication) through thecombination of medication intake, sleep data, and/or activity data. Inyet another example, the device or system may determine the efficacy ofan allergy medication through the combination of pollen data, medicationintake, sleep data and/or activity data. These examples are provided forillustration and not to be interpreted to be exclusive or limiting.

The user may employ more than one portable monitoring device. For a casein which the user tracks the same class of data during the same periodof time with more than one device, the two resulting data streams arecombined or synthesized as described more fully below according toseveral embodiments.

“Class of data” or “data class” refers to data that represents aquantifiable characteristic or a set of quantifiable characteristics inone or more ways. For example, one data class may be ambulatory stepscount. “Data type” refers to a specific representation of a quantifiablecharacteristic. Examples of data types may include walking steps perminute and running steps per minute. It is noted that both walking stepsper minute and running steps per minute may be considered a part of thedata class ambulatory step count.

FIG. 2 is a block diagram of a data synthesizer according to oneembodiment. Depending on the activity, a user may prefer informationfrom one device over the information from another device. For example,if the user is walking the user may prefer the information from the stepcounter, and if the user is swimming the user may prefer the informationfrom the GPS tracker. Sometimes, the user may prefer the GPS informationfrom the mobile phone instead of the GPS information from a fitnessmonitoring device. The process by which information from multipledevices is combined or filtered is referred to herein as data synthesis.

In the context of this disclosure, the term data synthesis or datastream synthesis is used to describe the combination of multiple datastreams into a single data stream or set of data streams using analgorithm or a set of algorithms. The use of the term “combination” doesnot imply that there are less data streams after data synthesis, in somecases the number of data streams inputted into a data synthesis systemor algorithm may be more or may be less than the number of data streamsoutputted. Data synthesis may be performed on a central device (e.g., asmartphone, a biometric hub such as an activity watch, a personalcomputer, a tablet computer, a home gaming console, a household sensor(e.g., thermostat)), through a web-based portal (e.g., www.fitbit.com),or via a combination of a central device and a web-based portal. Any andall aspects of distributed computing, remote data storage, virtualmemory, and cloud computing can be used for data synthesis in variousembodiments.

In the context of this disclosure the term data synthesizer will be usedto describe a computing device that is configured to accept data fromtwo or more devices and to combine their data into a single data set, orto generate an aggregate metric from similar or corresponding metricsfrom respective devices.

In one embodiment, the data synthesizer 202 includes data receiver 204,data parsing engine 206, data stream processing/synthesis module 208,and data export module 210. The data receiver 204 receives informationfrom sensor devices 214 or from other intermediary devices 212. Anintermediary device 212 is a device that receives data from one entityand forwards the data to another entity. In one embodiment, theintermediary device 212 is a mobile phone, but other embodiments mayutilize other types of devices capable of sending and receiving data. Asensor device 214 is any device able to capture user related informationregarding user activities, user biometric measurements, or userenvironmental parameters.

Data parsing engine 206 receives a stream of data and analyzes thestream of data to identify data types and data values. More details areprovided below with reference to FIGS. 3-5 regarding embodiments ofseveral parsing engines, also referred to as parsers.

Data stream processing/synthesis module 208 analyzes multiple streams ofdata and utilizes different algorithms and rules for performing datasynthesis on the multiple data streams. The output from the data streamprocessing/synthesis module 208 is passed to a data export module 210that is able to transfer the synthesized data to all other modules orother devices. For example, in one embodiment, data export module 210transfers the data to a computing device 216 for visualization of thedata for presentation to the user. For example, the data may bevisualized on a laptop or a smart phone.

It is noted that the embodiments illustrated in FIG. 2 are exemplary.Other embodiments may utilize different modules for data synthesis, orcombine the functionality of several modules into a single module. Theembodiments illustrated in FIG. 2 should therefore not be interpreted tobe exclusive or limiting, but rather exemplary or illustrative.

FIG. 3 is a block diagram of a data parser according to one embodiment.The data synthesizer may use one or more methodologies to facilitatedata input from sensor devices and other sources including, but notlimited to, third party devices and third party food logging websites(e.g., MyFitnessPal). These methodologies allow the data synthesizer todetermine which data streams represent the same type of information,regardless of the data stream source.

In one embodiment, a configurable data parsing engine 302, also referredto herein as configurable data parser, is used to accept input data 304from multiple sources. The configurable data parser 302 includes a datareceiver 308, a data parsing engine 312, and a configuration filelibrary 314. The data receiver 308 handles communications with otherdevices to receive input data 304. Further, the configuration filelibrary 314 includes files used to configure the data parsing engine312, also referred to simply as the parser. When the source sends datato the data synthesizer, the parser is properly configured to read datafrom that specific source using the appropriate configuration file. Theproper configuration file may be selected based on characteristics ofthe data stream source including, but not limited to, the device model,device identity, and/or an identifier in the data stream itself.

FIG. 4 is a block diagram of an input specific parser library accordingto one embodiment. In another embodiment, the data synthesizer may use alibrary of parsers to facilitate data input from sensor devices andother sources. The proper parser may be selected based oncharacteristics of the data stream source including but not limited tothe device model, device identity, or an identifier in the data stream.

The input specific parser library 402 includes data receiver 404, inputcompatible parser engine 406, and parser library 408. The inputcompatible parser engine 406 selects one of the parsers from the parserlibrary 408 in order to parse a specific data stream being provided asinput data 304. Once the data is parsed with a specific parser, theoutput is provided as output data 306.

FIG. 5 is a block diagram of a standard data format parser according toone embodiment. In the embodiment of FIG. 5, each data source adheres toa standard data format, enabling the data synthesizer 502 to use asingle data parser 508 for all data sources. A standard data format, orset of standard data formats, may be used with a configurable parser orlibrary of parsers as well. For example, the data synthesizer may have alibrary of configuration files for a configurable data parser whichincludes configuration files for each data source class. Data sourceclasses may include, but are not limited to, pedometer, scale, sleeptracker, online data entry, etc. Each of these classes may have astandard data format which can be read by a parser using the appropriatedata source class configuration file. Formats such as XML and JSON mayprovide the framework for a standard data format, in some embodiments.

Some standard data formats require the data stream to specify thefrequency of the data measurements or “sampling frequency.” Depending onthe data type and the device that is recording the data, a data pointmay represent a short or long period of time. For example, heart rate isa data type that is sampled at a high enough rate to measure heart ratevariability. In one embodiment, the device recording heart rate dataincludes a monitor with a sampling rate of tens of hertz. As acontrasting example, blood glucose level and blood pressure are datatypes measured with a frequency of minutes to months.

The spectrum of sampling frequencies may be separated into two mainbins. One bin is defined for any data that is typically sampled with afrequency on the order of daily or higher; data measured with thisrelatively low frequency sampling rate is referred to herein as“discrete” data. A second bin is defined for any data that is typicallysampled with a frequency on the order of once per minute or shorter;data measured with this relatively high frequency sampling rate isreferred to herein as “continuous” data. Continuous data may be acquiredcontinuously (24/7), or for shorter periods of time, including but notlimited to, 8 hours out of the day during week days. In otherembodiments, data is separated into more than two bins based on itssampling frequency. Indeed, the data may be separated into an almostunlimited number of bins where the bin of a data type is defined by itssampling frequency.

The use of sampling frequency here refers to the frequency of deriveddata points, not the frequency of the raw sampling data. For example, adevice using bioelectrical impedance analysis (BIA) may measure theimpedance of a user with a sampling rate of 10 Hz and then analyze thisdata to create a single body fat measurement, as is done by a weightscale and body composition monitor, for instance. The sampling frequencyof this data in this context would be the frequency with which body fatmeasurements were taken, not the raw BIA measurement rate.

In other cases these two rates may be equal. Other cases where thefrequency of derived data points differs from that of the raw samplingdata may also include data acquired over a period of time or activity inorder to create a representative measurement of the whole period of timeor activity. For example, an EKG strap may take many heart ratemeasurements during a run. At the end of the run, all of thesemeasurements may be used to derive one or more metrics of the user'sheart rate during the run including but not limited to the average heartrate, minimum heart rate, maximum heart rate, and maximum heart rateacceleration. In another example a GPS device may record a set of timestamped locations during a run. These locations may be used to derivethe length of the run and the user's average speed.

An example of a continuous data type is a data stream for the distancewalked in a minute. In a standard data format, this data type may have aspecific name, for example “Walk_Speed.” A standard data format may alsorequire a standard unit. In the case of this example, feet per minute isan applicable standard measurement unit. The data type may also definethe period over which the measurement is sampled, in this case oneminute.

A standard data format may also require a timestamp. For example, eachitem of data logged may be assigned a timestamp of the formYear.Month.Day.Hour.Minute.Second. The standard data format may includeother fields including, but not limited to, device manufacturer, deviceage, device accuracy, device precision, device model, and the locationwhere the device is located or worn (wrist, shoe, belt, pocket, etc.).

Table 1 below provides examples of data types that can be recorded oroperated on by the data synthesizer, portable biometric devices, and anyintermediary computing devices (such as clients). While the data typesare classified into biometric, environmental, continuous, and discretecategories, any data listed may be used in any of the listed categories.For example blood glucose is commonly measured 1-3 times a day in adiscrete manner using test strips. Blood glucose may also be monitoredcontinuously with a continuous blood glucose monitor. Many other datatypes not listed here may be used by the embodiments, and the listinghere is not intended to be exhaustive or limiting.

TABLE 1 DATA TYPES Biometric Environmental Continuous DiscreteContinuous Discrete steps weight location Raining elevation gained BMIelevation Snowing floors gained BMR temperature calories burned distancetraveled indoor vs speed SpO2 outdoor velocity blood pressure watchingTV heart rate arterial stiffness UV exposure heart rate while bloodglucose Ambient noise asleep levels Air quality heart rate while bloodvolume sedentary heart rate recovery heart rate time variability fallevent respiration rate shower detection SpO2 sleep apnea detectionstress cardiac arrhythmia activity level cardiac arrest detection pulsetransit time sitting/standing Distance of an detection activity muscletension Distance of a run blood flow Distance of a walk laugh detectionDistance of a bike respiration type- ride (snoring, breathing) Distanceof a swim breathing problems Duration of an user's voice activityswimming detection Duration of a run heat flux Duration of a walk sleepphase Duration of a bike detection ride typing detection Duration of aswim Duration of a workout Resting heart rate Maximum heart rate Activeheart rate Hydration (extra cellular water) Muscle mass Body fat mass

Methodologies for parsing input data streams, as described herein, maybe used to feed back output data or device configurations from the datasynthesizer to sensor devices or other services or computing devices.For example, the data synthesizer may use an unparser or serializer toformat a data package to be sent back to a sensor device. This datapackage may contain updated user statistics such as step count,notifications for the user (e.g., new message, goal met, friendrequest), and device configuration updates (e.g., newly calibratedstride length).

In various embodiments, data from third parties is accepted (read andprocessed) by the data synthesizer. In this context, third party refersto a company or individual which is independent of the company orindividual that manages and/or created the data synthesizer. Forexample, the company Fitbit may manage the data synthesizer and acceptdata from a third party company's devices or services such asMyFitnessPal™ or Garmin™. In one embodiment, third party data streamsare accepted if the data streams meet a set of requirements to ensurethat the third party data is reliable and properly handled. Theserequirements include adherence to a standardized data structure andidentification format. Other requirements may include, but are notlimited to, the data stream coming from a reputable or verified source,a certain level of accuracy, precision, and/or frequency in themeasurement of data. In an embodiment, another requirement is theexplicit consent of the user to allow the third party data to becombined with their existing data streams.

FIGS. 6-8 are diagrams of user interface screens according to severalembodiments. In one embodiment, data streams are combined using ahierarchical prioritization structure. A user's account may be loadedwith the hierarchical prioritization structure by default. In thiscontext, a user account refers to an online account which provides arepository for user's data. In one embodiment, the user account ismanaged by a server. A user account may also provide visualization ofuser data through a website, for example.

In one embodiment, the user is able to modify the user data through sucha website as shown in FIG. 6. Each data class collected by each devicemay be given priority, either above or below any other device'scorresponding data class. For example, if a user has a biometricmonitoring device clipped to their waistband and another biometricmonitoring device located in their shoe, where both measure steps taken,the data class “Step Count” may give a higher priority to the devicelocated in the shoe if the device is more accurate at counting steps. Ifboth devices also measure altitude gained, data from the monitoringdevice on the waistband may be given priority if the device is moreaccurate for the “Altitude Gained” class of data. In this case, theshoe's pedometer “Step Count” data class has priority over the waistbandpedometer. However, the shoe's pedometer “Altitude Gained” data classhas a lower priority than the waistband pedometer. This illustrates thata single device may have different priority assignments for differentdata classes. This method may be extended to a plurality of devices anddata classes.

In another embodiment, a user wears multiple devices in multiplelocations in or about the body. In some cases, more than one device isworn in the same location. Examples of locations where devices are wornor carried include, but are not limited to: on a hat, a headband, anearring, a necklace, an upper arm band, a lower arm band, a wristband, abelt, a ring, around the chest (as in a chest band), the waist band, apocket (shirt, coin, pants, jacket, etc.), shirt collar, ankle, shoe,etc. For example, a user may wear a device on their wrist (e.g., in awatch or bracelet) and a device on their waistband. In this case, thewrist-mounted device data is prioritized over the data of the waistbandfor swimming stroke rate if the wrist-mounted device is more accuratefor that specific data type. In another example, the waistband-mounteddevice step data is prioritized over the wrist-mounted device step data.

The default prioritization hierarchy may be determined solely by theaccuracy of the streams. In another embodiment, one or more additionaldata stream characteristics may be used to determine the defaultprioritization hierarchy including but not limited to precision,measurement frequency, and syncing frequency. In some cases, users areable to modify the default prioritization hierarchy. Data regardingusers' changes to the default prioritization hierarchy may be used tomodify the default prioritization hierarchy for new users.

In an embodiment, the user has the ability to change the defaultprioritization hierarchy, as shown in FIG. 6. This action can beperformed through one or multiple monitoring devices, through clientsoftware on a computing device, or through a web browser or Internetapplication on a computing device. The user can choose 602 to changedata stream prioritization 604 permanently, temporarily, or on an eventbasis (e.g., a single walking exercise). In the case that the change inpriority is temporary, the user can choose the duration that the changeis active.

In the embodiment shown, the user interface graphically displays (in aline graph in this example) the different data streams the user canchoose from. In an embodiment, the user interface is part of a web page.Alternatively, the user interface is a feature of software, or anapplication on a computing device such as a laptop, desktop computer, ormobile computing device such as a smartphone or tablet.

In the embodiment of FIG. 7, the user may prioritize the different datastreams 702 by using a drag-and-drop interface. In order to change thepriority of the data stream, the user clicks on the selected datastream, and while holding the click, the user moves the data stream upor down within the list of data streams.

In another embodiment, data stream prioritization is providedautomatically through heuristic rules, also referred to herein simply as“rules.” For instance, the maximum, minimum, or mean of two conflictingpieces of data are taken as an estimate. The heuristic rules are subjectto various criteria such as, for instance, a quality metric derived fromthe devices. For example, a quality metric for location data originatingfrom a GPS device can be derived from the number of satellites acquired.In the case of Wi-Fi and cell tower location determination, the numberof Wi-Fi routers and cell phone towers, the certainty of their position,and their proximity to the user may determine the quality metric.

In some cases devices have a measurable, known quality metric that isconsidered static or unchanging. For example a Dual X-Ray Absorptiometry(DEXA) body fat measurement device may have a well-defined margin oferror in its measurements. A user's body fat may also be determined byBioelectrical Impedance Analysis (BIA) which may also have awell-defined margin of error in its measurements. If there is data froma DEXA scan and a BIA measurement for the same time period, the DEXAdata may be prioritized because it has a higher accuracy. The BIA devicemay be calibrated or adjusted with the data of the DEXA. DEXA is justone example of a body fat measurement method. Hydrostatic weighing isalso suitable as a high accuracy body fat measurement method.

A single device may have varying quality metrics based on how the deviceis used. When a quality metric is used to determine the prioritizationof the data stream, the same device may have varying data streampriority based on how the device is used. For example, a blood pressurecuff measurement, when worn on the wrist, may be given a lower qualitymetric, and/or data stream priority, than a blood pressure cuffmeasurement on the arm. Similarly, an optical heart rate measurement ofthe finger may be given a higher quality metric, and/or data streampriority, than a measurement made by the same device on the wrist, as ameasurement on the finger is likely to have a higher accuracy. Thelocation where the device is worn may be determined though methodsincluding but not limited to those described in U.S. Pat. No. 8,386,008.

In certain cases, data from one device may overwrite data from the samedevice acquired at a different period of time if certain conditions aremet. In one example, the conditions for overwriting data may requirethat the overwriting data have a higher quality metric and be within acertain time window of the overwritten data. For example, a user takes ameasurement of their blood pressure at 8:45 am wearing the cuff on theirwrist, and then takes another measurement of their blood pressure at9:00 am wearing the cuff on their upper arm. The measurement made at9:00 am can overwrite the previous measurement if the conditions foroverwriting data are that a new measurement must be within 30 minutes ofthe one being overwritten and that it must have a higher quality metric.

FIG. 8 is a user interface screen showing data from all sources mergedand displayed as a graph, according to one embodiment. The data for FIG.8 is collected over the course of a user's day, as illustrated belowwith reference to FIG. 9. FIG. 8 shows the level of activity of the userover a 24-hour period. A graph bar shows the level of activity, and foreach 15-minute interval the level of activity is represented accordingto the size of the bar.

Several metrics can be displayed on the screen, and buttons on the topallow the user to select the metric, such as steps, distance, caloriesburned, location (GPS), floors/elevation, heart rate, and sleep.

Additionally, the charge may be annotated based on known activities ofthe user, such as commuting, working out, walking the dog, etc. Further,if the user clicks or hovers the mouse pointer over a period of time, aninformation window presents more details on the activity, such as thelocation, the actual number of steps during that interval, elevationgain, etc.

On the right side, the user is able to select which streams, from aplurality of possible streams, to display on the bar chart. In oneembodiment, multiple values can be represented simultaneously on thegraph bar, such as for example using different colors for each of theparameters. The streams may be labeled according to the device thatobtained the data and according to the type of data, such as steps,location, or heart rate. Additionally, a button is provided for the userto add new streams of data.

It is noted that the embodiments illustrated in FIG. 8 are exemplary.Other embodiments may utilize different layouts, data charts, datapoints, option buttons, data streams, etc. The embodiments illustratedin FIG. 8 should therefore not be interpreted to be exclusive orlimiting, but rather exemplary or illustrative.

FIG. 9 is a diagram illustrating data that could be collected during aperson's daily routine. The one or more tracking devices may capturedifferent types of data and identify different states of the user. Forexample, the system may identify when the user is sleeping, commuting,working, outdoors, running, at the gym, etc.

For each of the states of the user, different biometric andenvironmental parameters may be captured, such as the pollen in the air,the amount of carbon monoxide in the air, how many hours the user slept,how much time the user spent commuting, how much activity took placeduring the workday, how much solar radiation exposure, how many stepsclimbed, etc.

As the user goes about their day, the user may utilize differenttrackers. Sometimes, the user may have multiple devices tracking useractivities, such as a pedometer, a mobile phone, the step tracker, etc.

Embodiments presented herein consolidate the information obtained frommultiple devices to provide the best available picture to the user aboutthe user levels of activity, activities associated there with,environmental factors, etc. However, when the same information isprovided by two different trackers, there can be two different datapoints and the system provides heuristics to determine what is the bestdata point, or if a combination of the data points is the best approach.For example, if two different devices are tracking the heart rate of theuser, in one embodiment, the consolidated data includes the highestpeaks and valleys of the heart rate chart.

Sometimes a statistical combinations or measurements are used toconsolidated data. For example, the consolidated data may include one ormore of average, medium, maximum, or minimum of the multiple datapoints.

FIG. 10 is a block diagram of data stream synthesis according to oneembodiment. In one embodiment, a user prefers to wear a specific deviceduring the day, for example one which is clipped to the belt, and adifferent device at night which is more comfortable for sleeping, suchas a wrist band. The data synthesizer can use the belt clip device dataduring the day time, when the wrist band shows no activity, and thewrist band device data during the night, when the belt clip shows noactivity. In such a case, the two devices may not be given a “priority”over each other. Indeed, the data synthesizer may not have anyprioritization structure at all. Instead, in one embodiment, datastreams may be selected solely on the activity level.

In some cases, device data stream priority is used in addition toactivity level to determine the selected data stream. In one embodiment,a user wears a belt clip device and an anklet device during the day, anda belt clip device and a wrist band device at night. Assuming the ankletdata stream is given priority 1, the wrist band priority 2, and the beltclip priority 3, the data synthesizer shows the data from the ankletdevice during the day because the anklet device data has the highestpriority of the active devices for that time period, and the wrist banddevice at night because the wrist band device has the highest priorityof the active devices for that time period. The anklet has a higherpriority than the wrist band, but the anklet is not active at night.

A similar scheme is applicable to gaps in data due to syncing delays.For example, a user uses a pedometer and another device that measuresthe distance a user walks. The pedometer may have synced more recentlythan the other device. The device that has not synced recently may havea higher priority for the “distance walked” class of data (for example,a GPS device vs. a pedometer). In this case, the pedometer data is usedfor the time period between the time of the last GPS device sync and thetime of the more recent pedometer sync. When the GPS data syncs, thedata synthesizer can replace the distance data from the pedometer withthat of the GPS.

In one embodiment, the system stores all the sensor data (either locallyin the device or remotely in the server), so it is possible to reconcilethe data stream for different use cases when new data is available, evengoing back in time and reconcile historical data with the newlyavailable data. In one embodiment, a storage database keeps all the datacollected, as well as the reconciled data, also referred to herein asconsolidated data, which is the result of combining two or more datastreams. Further, it is possible to do further reconciliation ofhistorical data by going back to a specific time period and request thesystem to perform a reconciliation of the multiple data streams.

FIG. 11A is a block diagram of data stream synthesis according to oneembodiment. In one embodiment, multiple data streams of the same typeare combined using one or more methods. One method of combining datastreams of the same type is to average them. Other methods include aweighted average where the weight of one data stream over another isproportional to its accuracy. For example, if the two or more datastreams have a probabilistic measure of accuracy, then the combinedestimate is taken as the expectation. It may also be taken as the mostlikely (i.e., the value with the highest probability of being correct).

The algorithm that is used to combine data streams changes depending onvarious factors including the number of data streams, the quality ofdata streams (precision and accuracy), and the source of the datastreams (which model device, first party or third party, etc.). In someembodiments, the algorithm uses rules that define priorities for thedata streams and methods of combining the data from the multiple streamsto create a consolidated data stream having information from one or moremultiple sources.

When one device does not acquire data for a certain period of time, thedata synthesizer is able to maintain a continuous data stream by fillingin the gap with data from other devices. The data synthesizer can fillin the gap of data even if one device's data has a lower priority thananother device that was not acquiring data. In some cases, more than onedevice may have the same priority. In such cases, the device that iscurrently measuring data can be used. This is useful in cases when onedevice is not able to measure data, for example because of low battery.

In one embodiment, the data stream which has data that showscharacteristics of being used is selected by the data synthesizer. Forexample, for the step counts data stream, the data stream which has stepcounts greater than zero is selected because having more than zero stepsis characteristic of the device being used. In another example, anaccelerometer data stream shows characteristics of being worn when acertain pattern of accelerations is detected.

FIG. 11B illustrates an exemplary architecture of the data synthesizer,according to one embodiment. In one embodiment, the data synthesizer 202includes rule processor 1102, rules database 1104, the GPS module 1106,a timekeeper module 1108, a data selector/combiner module 1110, and adata stream database 1112.

Data synthesizer 202 receives one or more data streams A, B, C,environmental data, and any other type of external data that may haveinformation regarding user activities (e.g., user calendar). The ruleprocessor identifies the incoming data from the data streams,environmental data, and other data, and analyzes the pertaining rules toapply them to the incoming data. In one embodiment, the rules database1104 keeps track of the rules, but the rules may be stored elsewhere andmight be change over time to fine-tune the performance of the datasynthesizer 1102. In one embodiment, the user is able to create rulesfor prioritizing data, such as identifying the user's favorite (i.e.,highest priority) device.

After evaluating the different rules, the data selector/combinerconsolidates, also referred to as reconciles, the multiple data streamsto create a consolidated data. As used herein, consolidated data refersto the analysis of data received from multiple sources to generate adata source with information available for presentation to the user. Forexample, consolidated data may include filling gaps in the data streamfrom one device with data from another device, selecting which data tochoose when multiple sources of data provide information about the sameevent (e.g., step counts during a certain time interval), or anycombination thereof.

For example, a user may have three different data streams from threedifferent trackers. One of them may indicate that the user is at work,while the other two indicate that the user is at home. However, the ruleprocessor 1102 checks the user's calendar, and the calendar indicatesthat the user is at work, so the data selector 1110 may use the datafrom the tracker that is in accordance with the calendar, even thoughthat tracker may be in the “minority.” Therefore, external data may beused to determine the priority of different data sources. Also, externaldata may be used to determine priority with data that is not in the datastream.

In another example, the data synthesizer has access to calendar andcontact information for the user. The contacts may have addressesidentifying a location, and based on the physical location of thecontact, the data synthesizer may derive that the user is at the countryclub, so walking activity may be associated with the activity of playinggolf. In this case, the data from a tracker may be given higher prioritywhen considering that the user is playing golf.

Any method for capturing data may be used for the embodiments. Forexample, data may be acquired via and API to access a web service (e.g.,a web service that captures data for one of the trackers of the user).If the user provides login information for the external website, datasynthesizer 202 may access the web service using an API or some otherdata exchange mechanism.

In one embodiment, the data is reconciled as the data comes in, that isthe data is reconciled “on-the-fly,” and the combined, or consolidated,data stream is provided as an output stored in data stream database1112. In some embodiments, the data is reconciled during predeterminedtime periods (e.g., at night once a day). Sometimes there are delayswhen capturing the data, so the actual reconciliation with all theavailable data is done when all the data, or at least data from aplurality of devices, is available. In some embodiments, the same datamay be reconciled multiple times; as more data becomes available, newreconciliations may overwrite previous reconciliations.

In one embodiment, data stream database 1112 is a time series database.A time series database server (TSDS) is a software system that isoptimized for handling time series data, such as arrays of numbersindexed by time (e.g., a date-time or a date-time range). Sometimes thetime series are called profiles, curves, or traces. TSDS are oftennatively implemented using specialized database algorithms. However, itis possible to store time series as binary large objects (BLOBs) in arelational database or by using a VLDB approach coupled with a pure starschema. Efficiency is often improved if time is treated as a discretequantity rather than as a continuous mathematical dimension. Databasejoins across multiple time series data sets is only practical when thetime tag associated with each data entry spans the same set of discretetimes for all data sets across which the join is performed. The TSDSallows users to create, enumerate, update and destroy various timeseries and organize them in some fashion. These series may be organizedhierarchically and optionally have companion metadata available withthem. The server often supports a number of basic calculations that workon a series as a whole, such as multiplying, adding, or otherwisecombining various time series into a new time series. The server canalso filter on arbitrary patterns defined by the day of the week, lowvalue filters, high value filters, or even have the values of one seriesfilter another. Some TSDSs also provide statistical functions forsynthesizing multiple sources of data.

In one embodiment, the data streams are saved in the time seriesdatabase as the data comes in. In addition, summary series areprecalculated in some embodiments; for example, summaries can becalculated for reconciled data for one hour, two hours, daily, weekly,etc.

The use of a time series database facilitates the easy extraction ofdata from the database to present many types of graphs or many types ofdata made available to the user. For example, a user wants a count ofall the steps taken in a year. In one embodiment, the step count isstored as minute data, meaning that the number of steps taken in oneminute is stored in the database together with the timestamp. In thiscase, the user has data for three different trackers that have been usedthroughout the year. If the system calculates the summary for each ofthe data streams separately, the final step count may be different foreach of the devices, among other reasons, because some devices may notbe in use while other devices are in use. In order to get the reconciledtotal, a reconciliation is made for these three data streams to obtain aresulting data serious that accounts for the step counts throughout eachinterval (e.g., a minute). In other words, the reconciliation calculatesthe step count for each minute of the year by looking at the dataprovided for that minute by all three devices. Once the reconciliationis made by minute, the total for the year is calculated and presented tothe user.

As the data comes in, the data synthesizer 202 makes the determinationof whether to store all the incoming data, or reconcile the incomingdata, or both.

It is noted that the embodiments illustrated in FIG. 11B are exemplary.Other embodiments may utilize different modules or group the modulesinto other configurations, as long as the described functionality isprovided by data synthesizer 202. The embodiments illustrated in FIG.11B should therefore not be interpreted to be exclusive or limiting, butrather exemplary or illustrative.

FIG. 11C is a flowchart of a method for combining multiple data streams,according to one embodiment. In operation 802, device data is obtainedfrom one or more tracking devices. From operation 802, the method flowsto operation 804 where additional data is obtained, such asenvironmental data (time, temperature, whether, barometric pressure,etc.). However, in one embodiment, operation 804 is optional and thedata reconciliation only applies to data streams from tracking devices.

From operation 804, the method flows to operation 806 where the rulesfor combining or selecting data are obtained. In one embodiment, therules are kept in rules database 1104 of FIG. 11B, but in otherembodiments, the rules database may be stored in other places, such asin the server memory.

From operation 806, the method flows to operation 808 where the useractivity is identified for one or more time periods, based on theobtained data and the obtained rules. From operation 808, the methodflows to operation 810 where the system sets a directive for combiningor selecting data based on the rules and user activity. This directiveis used to identify the process by which data is to be combined from themultiple data streams.

From operation 810, the method flows to operation 812 where the datafrom the multiple data sources is combined or reconciled based on thedirective identified in operation 810. The result is an output datastream of consolidated data, also referred to as reconciled data.

The output stream is stored in operation 814. In one embodiment, theoutput stream is stored in data stream database 1112 of FIG. 11B. Inoperation 816 the output stream is provided to the user or to anothercomputing device (e.g., a smart phone).

While the various operations in this flowchart are presented anddescribed sequentially, one of ordinary skill will appreciate that someor all of the operations may be executed in a different order, becombined or omitted, or be executed in parallel.

FIG. 11D is an exemplary rules database according to one embodiment. Therules database includes one or more rules that identify a trigger (whichcan be a combination of one or more conditions) and an action (which canbe a combination of one or more actions). For example, a first ruleselects Device A when the user is running, a second rule selects DeviceB when the user is sleeping, a third rule calculate the average of thedata provided by devices A in C when the user is detected to be climbingsteps, etc.

Consider a user wearing a step tracker and a swim tracker. If the datafrom the swim tracker is present for a given interval with data from thestep tracker during the same interval, then the data of the step trackercan be removed (or at least not presented to the user) in favor of theswim data.

Consider a user using an activity tracker and a treadmill concurrently.The data synthesizer uses the following rules in an embodiment todetermine which data to select:

1. Treadmill distance overwrites tracker distance.

2. Tracker steps overwrites treadmill steps (if any).

3. Treadmill overwrites tracker stair gain (using slope information, orjust to 0 if the tracker counted any floors).

4. Treadmill calories overwrites tracker calories.

5. Treadmill distance measurement is used in the auto calibration oftracker stride length and/or calorie burn rate.

Similar structures are used for different exercise equipment, includingbut not limited to elliptical trainers, stair masters, stationary bikes,rowing machines, weight machines, etc.

In another embodiment, a swim tracker takes precedence over a steptracker, and overwrites calories, floor count, etc., (whether wrist,head, or body mounted). In another embodiment, a sleep trackeroverwrites steps (when the user is considered asleep), calories, andfloor count, from other devices. Other monitoring devices that areincorporated into similar data stream prioritization structures include,but are not limited to, free weight trackers (devices attached to freeweights or mounted on arms/body), sitting trackers (devices mounted inseat), sleep trackers (pillows/beds, motion/heart rate), alarm clocks(sound), head-mounted EEGs, and shoe trackers (inside the shoe orattached to the shoe for tracking distance/speed, sometimes including aGPS unit).

The algorithms for rule selection, operation, and modification may beself-learning or self-tuning in various embodiments. For instance, theuser may wear a device for calorie burn that is considered the “goldstandard” (i.e., preferred) device (because of its accuracy, forexample) over other devices. The system of devices or the individualdevices can develop a neural network (or other learning algorithm, ormerely adjust algorithmic parameters) to train the devices to match theoutput of the gold standard device.

What is considered the gold standard (i.e., with the highest priority)may change with the context of use. For example, data can be receivedfrom an elliptical machine and from a body-worn motion-based activitytracker. The body-worn activity tracker, in an embodiment, learns to (a)identify the motion signature that indicates use of an ellipticalmachine in order to automatically detect when the user is using anelliptical machine, and (b) use algorithms tuned for, or adapted to, theelliptical machine.

In another embodiment, the body-worn tracker has physiological sensors(e.g., heart rate, blood glucose, etc.) that are used for similarpurposes. In another embodiment, the use of a location aware portabledevice such as a GPS-enabled phone or tracker may detect that the useris driving, or is in a private or public vehicle such as a car, train,or bus. This information is used to correct erroneous step counts on astep-counting device and/or to train the step-counting device torecognize the motion signature of the vehicle. In one embodiment, thismotion signature is detected by sensing vibrational patterns with amotion sensor such as an accelerometer.

In various embodiments, the data synthesizer uses machine learning toolsincluding, but not limited to neural nets, k-means, nearest neighbor,support vector machine, naive Bayes, Bayesian network,expectation-maximization, and decision trees.

FIG. 11E illustrates the selection of different inputs, based oncombination rules, to generate a compound output, according to oneembodiment. Three data sources A, B, and C, provided data streams thatare being consolidated into a single output. In the first period, onlydata from Device A is available, therefore, the output is the data fromDevice A. Similarly, in the second period, only data from Device B isavailable so the output corresponds to the data from Device B.

In other periods, data may be available from two or more devices, andthe consolidated data may correspond to the data from one of thedevices, or to a combination of the data from the multiple devices. Forexample, in the fourth period, the output is the average of the datafrom devices A and B, and in the fifth period the output is the minimumof the data from devices B and C.

In one embodiment, a voting system is used to define the output. In avoting system, the output is selected by identifying data points thatare grouped in clusters, or in other words data points associated withthe value that corresponds to the approximate value provided by amajority of devices. For example, during a time period the followingstep counts, taken by multiple devices, are available: 22, 23, 20, and100.

The average value of these step counts would be 41.25, which isn'tconsistent with any of the data taken by any of the devices. Therefore,the average would not be an accurate measurement of the step count.Using a voting system, the system identifies that the majority of valuesare grouped around a cluster of around 22 steps. The value of 100 isdiscarded as being outside the cluster, and then the average of thevalues in the cluster (21.6), or the median (22), is selected as thedata point for the output. The voting system provides an algorithm forfiltering noise by discarding values that appeared to be anomalous.

In some embodiments, devices may learn to infer phenomena that thedevices cannot directly measure. For instance, if the user wears a heartrate monitor and a motion-based activity tracker, a correlation modelcan be made to infer a user's heart rate even when the user is notwearing the heart rate monitor (but the user is wearing the motion-basedactivity tracker). In another example, if a user wears a heart ratemonitor and a location aware device, such as a GPS, a correlation modelinfers the user's speed even when the user is not wearing the locationaware device. In another example, the distance measured by a locationaware device such as a GPS is used in combination with the number ofsteps measured by a pedometer to create an accurate measurement of theuser's stride length. This measurement is subsequently shared with theGPS device and the pedometer so that the GPS device can estimate theuser's steps taken, and so that the pedometer can estimate the distancethe user has travelled independently of the other device. In the casethat the GPS and the pedometer are integrated into the same device, theGPS can use the pedometer to create an estimate of the distance that theuser has travelled since the last satellite acquisition to aid indetermining the user's location.

In another embodiment, a plurality of devices is used to infer data thatno single device can accurately measure or infer. For example, if a useruses a heart rate monitor, a blood pressure monitor, and a bioelectricalimpedance analysis (BIA) device, the data synthesizer can infer theuser's blood pressure using a BIA measurement and a heart ratemeasurement.

FIG. 12 is a diagram of data stream synthesis from multiple devicesaccording to one embodiment. However, it should be noted that the datasynthesizer can also use data streams from multiple users to improve thedata quality of a single user.

In one embodiment, the data synthesizer uses the data streams of morethan one user to create statistics which are used to set defaults to asingle user's account. For example, if a user does not enter theirstride length data on their device or on the account associated with thedata synthesizer, a default stride length is set for them based ontypical stride lengths for users of similar age, height, and gender.Users can opt in or opt out of allowing the data synthesizer to usetheir data on other users' accounts. Users can also opt in or opt out ofallowing other users' data to modify their data.

In another embodiment, the data synthesizer uses a subset of all of theusers determined to be more relevant to the individual user. Forexample, the data synthesizer uses only data from users who have linkedaccounts through online social connections. Such “shared” data may beused in the method described above to determine a user's stride lengthif it is not entered by the user.

Other uses of shared data may involve an algorithm which determines whentwo users have performed the same activity together. For example, if twousers have location aware devices that also contain activity monitors,the data synthesizer determines that the two users have completed aworkout together, if the users were in a pre-defined proximity at thesame time they each had a similar activity level. Once the datasynthesizer determines that two users have completed a workout together,the data synthesizer uses the data of the respective users to improve oradd to the biometric data collected by each user. For example, if twousers go for a walk together, and user 1 had a device with an altimeterand user 2 did not, the data synthesizer can add the altimeter datastream from the account of user 1 for the duration of the walk to theaccount of user 2. The data synthesizer can also prompt both users toconnect socially online by “friending” each other, if the users have notalready. Further, the detection of a jointly performed workout or socialgathering may be awarded by the data synthesizer as in a virtual badgeor activity points.

In another embodiment, the accuracy or trustworthiness of individualsensors (or sensor models or types) is calculated by one or more of auser rating and prioritization settings in the system. The datasynthesizer may automatically rank the accuracy of connected sensors(e.g., a particular model of pedometer) based on the information fromthe data synthesis system. This ranked data may be shared with the userto recommend a more accurate or trustworthy sensor instead of thecurrent sensor. This data can also be used to train the defaultprioritization settings of new users.

The data synthesizer may export data in various ways. Data streams canbe viewed on a web site using any computing device with a web browser orappropriate mobile application (app). A user can choose to export theirdata streams to other devices or services. For example, a user may wantto have the synthesized data shown on one or more of their biometricmonitoring devices. The user may choose to have their data shared withvarious health monitoring services such as Microsoft Health Vault1M orMyFitnessPal™. The use of a standard data identification and structureaids in the compatibility of third party services, and so enhances theuser experience.

In an embodiment, the data synthesizer is able to determine which datastreams are the most accurate. With this knowledge, devices that haveincorrect data, or have data whose accuracy is lower than the mostaccurate data, have their data replaced with “better,” more accuratedata. For example, suppose a user has a pedometer device that usesinertial sensors to determine the distance a user has walked, and alsohas a GPS enabled device. The data synthesizer can determine that thedistance data as measured by the GPS enabled device is more accuratethan that of the inertial sensor pedometer. The data synthesizer canupdate the inertial pedometer with the more accurate GPS pedometer data.

In one embodiment, the determination of which data stream is mostaccurate is based on predetermined accuracies for specific sensors. Inanother embodiment, algorithms determine the accuracy of a data streamsolely based on the data stream's data and/or the data stream comparedto a second data stream which may indicate the accuracy of the firstdata stream. This can be extended to the comparison of more than twodata streams. For example, data from a GPS, inertial sensor pedometer,and location as determined by cellular tower triangulation may becompared to determine which is most accurate.

The data stream synthesizer can also create a function that relates adevice's raw data to a more accurate data stream or combination of datastreams. The data stream synthesizer compares a single device's datastream to a data stream that is more accurate. The data stream withhigher accuracy may be from a device that is known to have higheraccuracy, a device that is determined to have higher accuracy through acomparison with other data streams, or it may be from a combination ofmultiple data streams that together have a higher accuracy than anysingle data stream. The data stream synthesizer determines a functionalrelationship between a single device's data stream and the higheraccuracy data stream. This functional relationship is shared with thedevice so that the device can correct its algorithms and improve itsaccuracy. In one embodiment, this functional relationship ischaracterized as a calibration. In a case in which the device displaysdata, this allows the user to see higher accuracy biometric orenvironmental signals before these signals are uploaded and combined inthe data synthesizer.

In one embodiment, the data synthesizer automatically calibrates auser's stride length, or the function mapping of the user's stridelength (in the case that the stride length is a function of stepfrequency, foot contact time, surface slope, etc.). In an embodiment,this is accomplished by using the data stream of a GPS or other locationaware device to create an accurate measurement of the distance that theuser walks, runs, jogs, etc., in addition to a data stream from apedometer. The distance as measured from the GPS may also be used incombination with the number of steps measured by a pedometer to createan accurate measurement of the user's stride length.

In another embodiment, the system performs a discrete calibration event.In an embodiment, this involves the user uploading, sharing, or enteringhigh accuracy data with the data synthesizer. This data is then used tocalibrate any devices connected to the user's account. For example, auser uploads, enters, or authorizes the sharing of data that wascollected from a doctor's appointment using high accuracy equipmentincluding but not limited to data regarding blood pressure, body fat,resting heart rate, height and body weight. Such data may also be usedto retroactively correct a user's historical data.

The data synthesizer provides the synthesized data to a user interfaceso that the user can review data. In an embodiment, the user interfaceallows the user to perform such tasks as correcting incorrect data,entering biometric data, modifying data stream prioritization settings,entering quality metric data for data streams, setting goals, enteringdata that is not tracked by the user's devices and interacting sociallyand competitively with other users. The user interface can also allowthe user to quickly review and comprehend data from many differentdevices with varying sampling frequencies.

The data streams or algorithmic combinations of data streams may be usedby the data synthesizer or a computing device connected to the datasynthesizer (such as a server) to provide a rich interaction between theuser and their data, and between the user and the data of others. In oneembodiment the user is presented with a simplified view of all of theirdata streams. For example, a user's account website shows the user thetotal number of steps the user has as determined by a combination of allof their step counting data streams in a specific period of timeincluding but not limited to hour, day, week, month, year, and the timeperiod that the user has had an account. Other totals of data areselectively provided to the user in a similar manner including, but notlimited to calories burned, distance of walks and/or runs, floorsclimbed, hours asleep, hours in bed, hours of a specific activity orcombination of activities such as walking, biking, running, and/orswimming.

The user is able to compare user's data with the data of other users,and compete with other users or with themselves. For example, the usercan choose to be ranked according to one or more data streams in aleaderboard with other users that the users have “friended.” The user isable to compare their current data statistics with their own statisticsfrom a different time period. For example, a user is shown how many moreor less steps the user took the previous week compared to the currentweek. A user's data can also be used in virtual contests with set goals,goals based on the user's own statistics, or goals based on other user'sstatistics. Real or virtual prizes are awarded for winning such acontest. For example, a group of users competing in a corporate wellnesschallenge win prizes based on data acquired and combined by the datasynthesizer.

In an embodiment, a user is awarded a virtual badge for reaching acertain data metric using the data acquired and/or combined by the datasynthesizer. For example, a user is awarded a badge for climbing 50floors in one day. In another example, a user is awarded a badge fortaking a total of 10,000 steps since the user started acquiring stepdata.

FIG. 13 illustrates an interface on a portable device for configuringtracking devices in use by the user. In one embodiment, a website orapplication connected to the data synthesizer enables the user to managethe devices that communicate with the data synthesizer. Such amanagement tool is called a “Device Locker” in one embodiment. In thedevice locker, the user is able to see all the devices connected to hisaccount, as well as a variety of their characteristics, settings or dataincluding but not limited to battery level, model, name, and last timesynced.

The device locker can provide notification of when a device's batteryis, or is going to be, critically low. For example, the device lockernotifies the user with a pop up dialog stating that the device will onlybe able to track steps for two more hours before the battery runs out ofcharge.

In the example of FIG. 13, the user has configured a digital scale and awearable fitness tracker. If the user selects a device (e.g., touchesthe area associated with one device), further information regarding thedevice and its configuration is provided. A button at the bottom of theinterface provides an option for setting up new devices.

FIG. 14 is a user interface screen with a targeted advertisementaccording to one embodiment. In another embodiment, as illustrated inFIG. 14, it is determined that the user's device lacks some capabilityor feature that is available on another device. The other device isoffered for sale to the user in the form of a link to more information,including purchase information.

FIG. 15 illustrates an interface that provides information about useractivities and tracking devices. The interface includes an area with theactive devices, and in the embodiment of FIG. 15 there are three activetrackers, a wearable tracker and two different digital scales. For eachdevice, the interface provides information regarding the last time thatthe device was synced (e.g., 7 minutes ago, 6 hours ago, 21 hours ago)and how much battery is left on the device.

In the embodiment of FIG. 15, the battery level is indicated via an iconthat symbolizes a battery with a dark bar inside. The bar indicates howmuch power is left. A full battery would have a bar completely full(e.g., completely dark), and a empty battery would have an empty bar. Inother embodiments, the amount of battery left may be indicated in otherways, such as via a number of the percentage of battery left, or anumber indicating the amount of time left before the device runs out ofpower.

FIG. 16 illustrates an interface for managing a device on the webservice, according to one embodiment. When the user selects one of theactive tracking device (e.g., clicks or touches on the correspondingdevice in FIG. 15), the interface of FIG. 16 is presented withinformation about the selected device. In one embodiment, a list ofdevices is provided on the page and the user can select one device at atime to view the profile information for the corresponding device.

For example, the device settings for the device may include informationabout tracking steps, tracking distance, tracking calories burned,tracking floors climbed, setting the clock on the device, changing thegreeting on the device, etc. In addition, other options include settingthe device for left hand or right hand use, changing the firmwareversion, information about the battery level, the last time the devicewas synced, etc.

FIG. 17 is a diagram of a user account website page example in whichDevice A has the ability to measure or estimate the number of floorsthat a user has climbed. Metadata of a data stream or data streams, orcharacteristics of devices linked to a user's account are used inembodiments to improve the presentation of data to the user. Forexample, if a user has a device or devices that track steps and distancewalked, the user's account website is configurable to show only thosetwo metrics. In another example, if the user's devices can collect dataabout the user's sleep, but the user never or infrequently uses thisdevice feature, the user's account omits any metrics regarding sleep.FIG. 17 is a diagram of a user account website that shows an example inwhich Device A has the ability to measure or estimate the number offloors that a user has climbed. FIG. 18 shows the same data presentationwebsite for a different device which cannot track the number of floorsclimbed. In this case, the number of floors climbed is omitted from thedata presented.

In another example, FIG. 17 shows the website for a user which haslinked to his account a device that can track many metrics, but thatlacks features of another model, such as a newer form factor, theability to sync with a cellular device, and/or different aestheticqualities such as color. This data is used in choosing an advertisementfor this particular user. Other features that a user's device or devicesmay or may not have, but which could be used to inform the choice of anadvertisement include, but are not limited to, GPS tracking, heart ratemeasurements, sleep state tracking and logging, blood pressure trackingand logging, and specific activity statistic tracking and logging suchas swimming or biking.

Data about the user himself may also be used alone or in combinationwith device data to select advertisements. For example, if a user'sgender is female, devices advertised to her are better adapted to coupleto a female body and/or have aesthetics more commonly desirable for hergender. Metadata of data streams may also be used to inform theselection of an advertisement for a user. For example, if a user hasmade one or more manual data entries using their account website for aparticular data type which is not tracked by their device, this user maybe advertised a device which tracks that data type. In another example,a user who uses their device to track many different metrics andinteracts with the website frequently is determined to be a dataoriented user. Devices are advertised to this user that better cater todata oriented users, such as devices that have many features and requireor invite significant interaction from the user to take advantage of allof the device features. Conversely, a user that rarely interacts withtheir device or website may be advertised a device that requires verylittle user interaction to acquire data.

In another embodiment, the data synthesizer is able to send electroniccommands to other electronic devices based on data from one or multiplestreams. In one example, the data synthesizer receives data from alocation aware activity monitor in addition to a home thermostat. If thedata synthesizer determines that the user is coming home from anactivity, and that the temperature at home is not at a temperature whichis comfortable, the data synthesizer sends a command to adjust thethermostat to achieve a comfortable temperature before the user getshome.

In another embodiment, the data synthesizer sends a command to thethermostat to lower the temperature before a user arrives home from arun, so that the home is comfortably cool when the user arrives. Inanother example, the thermostat may be adjusted to a lower temperaturewhen it is detected that the user has left their home so as to saveenergy. This may be applied to multiple users of a household orapartment. For example, the thermostat may only be lowered when all ofthe users of the household are not home.

In another embodiment, the data synthesizer detects whether or not theuser is in a vehicle and/or driving a vehicle. There may be anoccupation detector in the car which communicates with the datasynthesizer. For example, a vehicle detects that the user is in thevehicle if the user's weight is detected by a sensor in a seat in thecar. The user is determined to be in a car by other means including butnot limited to an accelerometer signal (in a device worn by the user)which is determined to be characteristic of being in a vehicle, alocation signal which is characteristic of being in a vehicle (the usermay be going at car speeds in a location associated with a road, forexample), and a combination of multiple data streams characteristic ofdriving such as having a certain heart rate, stress level, and auditorysignal.

The data synthesizer can take action on one or more streams of datameasured while the user is in a vehicle. For example, if the datasynthesizer determines that the user is drowsy or otherwise in anunsafe-to drive condition based on one or more data streams includingbut not limited to heart rate and accelerometer signals, the datasynthesizer may alert the driver. The driver can be alerted by thedevice in one or more ways including but not limited to vibrating,making a sound, sending an auditory command, lighting up and/ordisplaying a message. In another example, the data synthesizer sends acommand to the thermostat of the vehicle to adjust the temperaturebefore or while the user is in the car.

FIG. 18 is a diagram of a website page for a device (e.g., a weightscale) which cannot track the number of floors climbed, according to oneembodiment. Data about the device or devices connected to a user'saccount can also be used to help create recommendations or determinerelevant advertisements about other devices. For example, in FIG. 18 theaccount owner does not have a device which can measure body weight andbody fat linked to their account. This information is used to helpdetermine the type of advertisement which may be most relevant to theuser, in this case, an advertisement for a weight scale.

FIG. 19 is a simplified schematic diagram of a device for implementingembodiments described herein. The monitoring device 152 is an example ofany of the monitoring devices described herein, and including a steptracker, a fitness tracker without buttons, or a fitness tracker definedto be clipped onto the belt of a user, etc. The monitoring device 152includes processor 154, memory 156, one or more environmental sensors158, one or more position and motion sensors 160, watch 162,vibrotactile feedback module 164, display driver 168, touchscreen 206,user interface/buttons 170, device locator 172, external event analyzer174, motion/activity analyzer 176, power controller 178, and battery180, all of which may be coupled to all or some of the other elementswithin monitoring device 152.

Examples of environmental sensors 158 include a barometric pressuresensor, a weather condition sensor, a light exposure sensor, a noiseexposure sensor, a radiation exposure sensor, and a magnetic fieldsensor. Examples of a weather condition sensor include sensors formeasuring temperature, humidity, pollen count, air quality, rainconditions, snow conditions, wind speed, or any combination thereof,etc. Examples of light exposure sensors include sensors for ambientlight exposure, ultraviolet (UV) light exposure, or a combinationthereof, etc. Examples of air quality sensors include sensors formeasuring particulate counts for particles of different sizes, level ofcarbon dioxide in the air, level of carbon monoxide in the air, level ofmethane in the air, level of other volatile organic compounds in theair, or any combination thereof.

Examples of the position/motion sensor 160 include an accelerometer, agyroscope, a rotary encoder, a calorie measurement sensor, a heatmeasurement sensor, a moisture measurement sensor, a displacementsensor, an ultrasonic sensor, a pedometer, an altimeter, a linearposition sensor, an angular position sensor, a multi-axis positionsensor, or any combination thereof, etc. In some embodiments, theposition/motion sensor 160 measures a displacement (e.g., angulardisplacement, linear displacement, or a combination thereof, etc.) ofthe monitoring device 152 over a period of time with reference to athree-dimensional coordinate system to determine an amount of activityperformed by the user during a period of time. In some embodiments, aposition sensor includes a biological sensor, which is further describedbelow.

The vibrotactile module 164 provides sensory output to the user byvibrating portable device 152. Further, the communications module 166 isoperable to establish wired or wireless connections with otherelectronic devices to exchange data (e.g., activity data, geo-locationdata, location data, a combination thereof, etc.). Examples of wirelesscommunication devices include, but are not limited to, a Wi-Fi adapter,a Bluetooth device, an Ethernet adapter, and infrared adapter, anultrasonic adapter, etc.

The touchscreen 206 may be any type of display with touch sensitivefunctions. In another embodiment, a display is included but the displaydoes not have touch-sensing capabilities. The touchscreen may be able todetect a single touch, multiple simultaneous touches, gestures definedon the display, etc. The display driver 168 interfaces with thetouchscreen 206 for performing input/output operations. In oneembodiment, display driver 168 includes a buffer memory for storing theimage displayed on touchscreen 206.

The buttons/user interface may include buttons, switches, cameras, USBports, keyboards, or any other device that can provide input or outputfunctions.

Device locator 172 provides capabilities for acquiring data related tothe location (absolute or relative) of monitoring device 152. Examplesdevice locators 172 include a GPS transceiver, a mobile transceiver, adead-reckoning module, a camera, etc. As used herein, a device locatormay be referred to as a device or circuit or logic that can generategeo-location data. The geo-location data provides the absolutecoordinates for the location of the monitoring device 152. Thecoordinates may be used to place the monitoring device 152 on a map, ina room, in a building, etc. in some embodiments, a GPS device providesthe geo-location data. In other embodiments, the geo-location data canbe obtained or calculated from data acquired from other devices (e.g.,cell towers, Wi-Fi device signals, other radio signals, etc.), which canprovide data points usable to locate or triangulate a location.

External event analyzer 174 receives data regarding the environment ofthe user and determines external events that might affect the powerconsumption of the user. For example, the external event analyzer 174may determine low light conditions in a room, and assume that there is ahigh probability that the user is sleeping. In addition, the externalevent analyzer 174 may also receive external data, such as GPS locationfrom a smart phone, and determine that the user is on a vehicle and inmotion.

In some embodiments, the processor 154 receives one or moregeo-locations measured by the device locator 172 over a period of timeand determines a location of the monitoring device 152 based on thegeo-locations and/or based on one or more selections made by the user,or based on information available within a geo-location-locationdatabase of the network. For example, the processor 154 may compare thecurrent location of the monitoring device against known locations in alocation database, to identify presence in well-known points of interestto the user or to the community. In one embodiment, upon receiving thegeo-locations from the device locator 172, the processor 154 determinesthe location based on the correspondence between the geo-locations andthe location in the geo-location-location database.

The one or more environmental sensors 158 may sense and determine one ormore environmental parameters (e.g., barometric pressure, weathercondition, amount of light exposure, noise levels, radiation levels,magnetic field levels, or a combination thereof, etc.) of an environmentin which the monitoring device is placed.

The watch 162 is operable to determine the amount of time elapsedbetween two or more events. In one embodiment, the events are associatedwith one or more positions sensed by the position sensor 160, associatedwith one or more environmental parameters determined by theenvironmental sensor 158, associated with one or more geo-locationsdetermined by the device locator 172, and/or associated with one or morelocations determined by the processor 154.

Power controller 178 manages and adjusts one or more power operationalparameters defined for the monitoring device 152. In one embodiment, thepower operational parameters include options for managing thetouchscreen 206, such as by determining when to turn ON or OFF thetouchscreen, scan rate, brightness, etc. In addition, the powercontroller 178 is operable to determine other power operationalparameters, besides the parameters associated with the touchscreen, suchas determining when to turn ON or OFF other modules (e.g., GPS,environmental sensors, etc.) or limiting the frequency of use for one ormore of the modules within monitoring device 152.

Monitoring device 152 may have a variety of internal states and/orevents which may dynamically change the characteristics of thetouchscreen or of other modules. These states may include one or more ofthe following:

Battery level

Notifications/Prompting of user interaction

-   -   Alarm    -   Timer elapsed    -   Email received/sent    -   Instant Message received/sent    -   Text message received/sent    -   Calendar event    -   Physiological goal met (e.g., 10,000 steps reached in the day)    -   Non-physiological goal met (e.g., completed a to-do item)    -   Application notifications    -   Music player notifications (e.g., song ended/started, playlist        ended/started)

User Interface

-   -   Layout of virtual buttons on the touchscreen    -   Expected user interaction based on what is displayed and/or the        application in the foreground of the operating system.        -   Expected user touch speed (e.g., fast for typing or playing            a game, slow for reading an article)        -   Expected user touch area        -   Expected user touch trajectory (e.g., some games require            long, straight swipes, while applications that take text            input may require a touch to one specific area with little            or no trajectory).

User interaction through non-touchscreen inputs

-   -   User pressing a button    -   User touching a capacitive touch sensor not integrated into the        touchscreen    -   User activating a proximity sensor    -   Sensors which detect the user attempting to interact with the        screen        -   Force transducer under the screen        -   Gyroscope, magnetometer, and/or accelerometer located near            the screen        -   Pressure transducer to measure change in pressure due to            housing deflection when user presses on or near the screen        -   Tap or initial touch detection using one or more or a            combination of: accelerometers, piezoelectric sensors,            motion sensors, pressure sensors, force sensors

User Characteristics

-   -   Gender    -   Age    -   Weight    -   Geographical location (e.g., urban area, rural area, country,        city, town, exact address and or GPS coordinates)        -   Current location        -   Residence location        -   Work location

It is noted that these states may be communicated to the user throughone or more methods including, but not limited to, displaying themvisually, outputting an audio alert, and/or haptic feedback.

In some embodiments, data analysis of data produced by different modulesmay be performed in monitoring device 152, in other device incommunication with monitoring device 152, or in combination of bothdevices. For example, the monitoring device may be generating a largeamount of data related to the heart rate of the user. Beforetransmitting the data, the monitoring device 152 may process the largeamount of data to synthesize information regarding the heart rate, andthen the monitoring device 152 may send the data to a server thatprovides an interface to the user. For example, the monitoring devicemay provide summaries of the heart rate in periods of one minute, 30seconds, five minutes, 50 minutes, or any other time period. Byperforming some calculations in the monitoring device 152, theprocessing time required to be performed on the server is decreased.

Some other data may be sent in its entirety to another device, such assteps the user is taken, or periodical updates on the location of themonitoring device 152. Other calculations may be performed in theserver, such as analyzing data from different modules to determinestress levels, possible sickness by the user, etc.

It is noted that the embodiments illustrated in FIG. 19 are exemplary.Other embodiments may utilize different modules, additional modules, ora subset of modules. In addition, some of the functionality of twodifferent modules might be combined in a single module, or thefunctionality of a single module might be spread over a plurality ofcomponents. The embodiments illustrated in FIG. 19 should therefore notbe interpreted to be exclusive or limiting, but rather exemplary orillustrative.

FIG. 20 is a flowchart illustrating an algorithm for performing datasynthesis for data provided by a plurality of devices, according to oneembodiment. In operation 852, a plurality of devices is associated witha user, each device being operable to capture data associated with theuser.

From operation 852 the method flows to operation 854, where captureddata is received from the plurality of devices, the captured data beingabout a first activity associated with a first period of time. Fromoperation 854 the method flows to operation 856, where a detection takesplace regarding the received captured data from two or more devicesprovides overlapping information about the first activity.

From operation 856 the method flows to operation 858, where one or morerules are evaluated, the one or more rules being for consolidating theoverlapping information to produce consolidated data. The consolidateddata provides a unified view of the first activity during the firstperiod of time.

From operation 858 the method flows to operation 860 for storing theconsolidated data for presentation to the user. In one embodiment, theoperations of the method are executed by a processor.

FIG. 21 is a flowchart illustrating an algorithm for creating a unifieddata stream from a plurality of data streams acquired from a pluralityof devices, according to one embodiment. In operation 864, a pluralityof activity data streams are received from a plurality of devices, eachactivity data stream being associated with physical activity data of auser.

From operation 864 the method flows to operation 866, which is anoperation for assembling a unified activity data stream for the userover a period of time. That unified activity data stream includes datasegments from the data streams of at least two devices of the pluralityof devices, and the data segments are organized time-wise over theperiod of time. As used herein, a time-wise organization of datasegments refers to the aggregation of data from multiple devices basedon the time associated with the data. In one embodiment, the dataincludes physical activity data of the user tagged with the timestampfor the time when the activity took place. As a result, the unifiedactivity data stream includes data segments with respective time tagsidentifying the time associated with the data segment.

From operation 866 the method flows to operation 868, which is anoperation for generating display data for the unified activity datastream. The display data is configured for presentation on aninteractive user interface, and the interactive user interface enablesuser selection of at least one of the data segments to identifyadditional information for the data segment, and to further identifywhich of the plurality of devices produced the physical activity data ofthe user for the selected data segment. In one embodiment, theoperations of the method are executed by a processor.

While the various operations in this flowchart are presented anddescribed sequentially, one of ordinary skill will appreciate that someor all of the operations may be executed in a different order, becombined or omitted, or be executed in parallel.

FIG. 22 illustrates an example where various types of activities ofusers 900A-900I can be captured or collected by activity trackingdevices, in accordance with various embodiments of the presentembodiments. As shown, the various types of activities can generatedifferent types of data that can be captured by the activity trackingdevice 100. The data, which can be represented as motion data (orprocessed motion data) can be transferred 920 to a network 176 forprocessing and saving by a server, as described above. In oneembodiment, the activity tracking device 100 can communicate to a deviceusing a wireless connection, and the device is capable of communicatingand synchronizing the captured data with an application running on theserver. In one embodiment, an application running on a local device,such as a smart phone or tablet or smart watch can capture or receivedata from the activity tracking device 100 and represent the tractmotion data in a number of metrics.

In one embodiment, the device collects one or more types ofphysiological and/or environmental data from embedded sensors and/orexternal devices and communicates or relays such metric information toother devices, including devices capable of serving asInternet-accessible data sources, thus permitting the collected data tobe viewed, for example, using a web browser or network-basedapplication. For example, while the user is wearing an activity trackingdevice, the device may calculate and store the user's step count usingone or more sensors. The device then transmits data representative ofthe user's step count to an account on a web service, computer, mobilephone, or health station where the data may be stored, processed, andvisualized by the user. Indeed, the device may measure or calculate aplurality of other physiological metrics in addition to, or in place of,the user's step count.

Some physiological metrics include, but are not limited to, energyexpenditure (for example, calorie burn), floors climbed and/ordescended, heart rate, heart rate variability, heart rate recovery,location and/or heading (for example, through GPS), elevation,ambulatory speed and/or distance traveled, swimming lap count, bicycledistance and/or speed, blood pressure, blood glucose, skin conduction,skin and/or body temperature, electromyography, electroencephalography,weight, body fat, caloric intake, nutritional intake from food,medication intake, sleep periods (i.e., clock time), sleep phases, sleepquality and/or duration, pH levels, hydration levels, and respirationrate. The device may also measure or calculate metrics related to theenvironment around the user such as barometric pressure, weatherconditions (for example, temperature, humidity, pollen count, airquality, rain/snow conditions, wind speed), light exposure (for example,ambient light, UV light exposure, time and/or duration spent indarkness), noise exposure, radiation exposure, and magnetic field.

Still further, other metrics can include, without limitation, caloriesburned by a user, weight gained by a user, weight lost by a user, stairsascended, e.g., climbed, etc., by a user, stairs descended by a user,steps taken by a user during walking or running, a number of rotationsof a bicycle pedal rotated by a user, sedentary activity data, driving avehicle, a number of golf swings taken by a user, a number of forehandsof a sport played by a user, a number of backhands of a sport played bya user, or a combination thereof. In some embodiments, sedentaryactivity data is referred to herein as inactive activity data or aspassive activity data. In some embodiments, when a user is not sedentaryand is not sleeping, the user is active. In some embodiments, a user maystand on a monitoring device that determines a physiological parameterof the user. For example, a user stands on a scale that measures aweight, a body fat percentage, a biomass index, or a combinationthereof, of the user.

Furthermore, the device or the system collating the data streams maycalculate metrics derived from this data. For example, the device orsystem may calculate the user's stress and/or relaxation levels througha combination of heart rate variability, skin conduction, noisepollution, and sleep quality. In another example, the device or systemmay determine the efficacy of a medical intervention (for example,medication) through the combination of medication intake, sleep and/oractivity data. In yet another example, the device or system maydetermine the efficacy of an allergy medication through the combinationof pollen data, medication intake, sleep and/or activity data. Theseexamples are provided for illustration only and are not intended to belimiting or exhaustive.

This information can be associated to the users account, which can bemanaged by an activity management application on the server. Theactivity management application can provide access to the users accountand data saved thereon. The activity manager application running on theserver can be in the form of a web application. The web application canprovide access to a number of websites screens and pages that illustrateinformation regarding the metrics in various formats. This informationcan be viewed by the user, and synchronized with a computing device ofthe user, such as a smart phone.

In one embodiment, the data captured by the activity tracking device 100is received by the computing device, and the data is synchronized withthe activity measured application on the server. In this example, dataviewable on the computing device (e.g., smart phone) using an activitytracking application (app) can be synchronized with the data present onthe server, and associated with the user's account. In this way,information entered into the activity tracking application on thecomputing device can be synchronized with application illustrated in thevarious screens of the activity management application provided by theserver on the website.

The user can therefore access the data associated with the user accountusing any device having access to the Internet. Data received by thenetwork 176 can then be synchronized with the user's various devices,and analytics on the server can provide data analysis to providerecommendations for additional activity, and or improvements in physicalhealth. The process therefore continues where data is captured,analyzed, synchronized, and recommendations are produced. In someembodiments, the captured data can be itemized and partitioned based onthe type of activity being performed, and such information can beprovided to the user on the website via graphical user interfaces, or byway of the application executed on the users smart phone (by way ofgraphical user interfaces).

In one embodiment, the sensor or sensors of a device 100 can determineor capture data to determine an amount of movement of the monitoringdevice over a period of time. The sensors can include, for example, anaccelerometer, a magnetometer, a gyroscope, or combinations thereof.Broadly speaking, these sensors are inertial sensors, which capture somemovement data, in response to the device 100 being moved. The amount ofmovement (e.g., motion sensed) may occur when the user is performing anactivity of climbing stairs over the time period, walking, running, etc.The monitoring device may be worn on a wrist, carried by a user, worn onclothing (using a clip, or placed in a pocket), attached to a leg orfoot, attached to the user's chest, waist, or integrated in an articleof clothing such as a shirt, hat, pants, blouse, glasses, and the like.These examples are not limiting to all the possible ways the sensors ofthe device can be associated with a user or thing being monitored.

In other embodiments, a biological sensor can determine any number ofphysiological characteristics of a user. As another example, thebiological sensor may determine heart rate, a hydration level, body fat,bone density, fingerprint data, sweat rate, and/or a bioimpedance of theuser. Examples of the biological sensors include, without limitation, abiometric sensor, a physiological parameter sensor, a pedometer, or acombination thereof.

In some embodiments, data associated with the user's activity can bemonitored by the applications on the server and the users device, andactivity associated with the user's friends, acquaintances, or socialnetwork peers can also be shared, based on the user's authorization.This provides for the ability for friends to compete regarding theirfitness, achieve goals, receive badges for achieving goals, getreminders for achieving such goals, rewards or discounts for achievingcertain goals, etc.

As noted, an activity tracking device 100 can communicate with acomputing device (e.g., a smartphone, a tablet computer, a desktopcomputer, or computer device having wireless communication access and/oraccess to the Internet). The computing device, in turn, can communicateover a network, such as the Internet or an Intranet to provide datasynchronization. The network may be a wide area network, a local areanetwork, or a combination thereof. The network may be coupled to one ormore servers, one or more virtual machines, or a combination thereof. Aserver, a virtual machine, a controller of a monitoring device, or acontroller of a computing device is sometimes referred to herein as acomputing resource. Examples of a controller include a processor and amemory device.

In one embodiment, the processor may be a general purpose processor. Inanother embodiment, the processor can be a customized processorconfigured to run specific algorithms or operations. Such processors caninclude digital signal processors (DSPs), which are designed to executeor interact with specific chips, signals, wires, and perform certainalgorithms, processes, state diagrams, feedback, detection, execution,or the like. In some embodiments, a processor can include or beinterfaced with an application specific integrated circuit (ASIC), aprogrammable logic device (PLD), a central processing unit (CPU), or acombination thereof, etc.

In some embodiments, one or more chips, modules, devices, or logic canbe defined to execute instructions or logic, which collectively can beviewed or characterized to be a processor. Therefore, it should beunderstood that a processor does not necessarily have to be one singlechip or module, but can be defined from a collection of electronic orconnecting components, logic, firmware, code, and combinations thereof.

Examples of a memory device include a random access memory (RAM) and aread-only memory (ROM). A memory device may be a Flash memory, aredundant array of disks (RAID), a hard disk, or a combination thereof.

Embodiments described in the present disclosure may be practiced withvarious computer system configurations including hand-held devices,microprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers and the like. Severalembodiments described in the present disclosure can also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a wire-based or wirelessnetwork.

With the above embodiments in mind, it should be understood that anumber of embodiments described in the present disclosure can employvarious computer-implemented operations involving data stored incomputer systems. These operations are those requiring physicalmanipulation of physical quantities. Any of the operations describedherein that form part of various embodiments described in the presentdisclosure are useful machine operations. Several embodiments describedin the present disclosure also relate to a device or an apparatus forperforming these operations. The apparatus can be specially constructedfor a purpose, or the apparatus can be a computer selectively activatedor configured by a computer program stored in the computer. Inparticular, various machines can be used with computer programs writtenin accordance with the teachings herein, or it may be more convenient toconstruct a more specialized apparatus to perform the requiredoperations.

Various embodiments described in the present disclosure can also beembodied as computer-readable code on a non-transitory computer-readablemedium. The computer-readable medium is any data storage device that canstore data, which can thereafter be read by a computer system. Examplesof the computer-readable medium include hard drives, network attachedstorage (NAS), ROM, RAM, compact disc-ROMs (CD-ROMs), CD-recordables(CD-Rs), CD-rewritables (RWs), magnetic tapes and other optical andnon-optical data storage devices. The computer-readable medium caninclude computer-readable tangible medium distributed over anetwork-coupled computer system so that the computer-readable code isstored and executed in a distributed fashion.

Although the method operations were described in a specific order, itshould be understood that other housekeeping operations may be performedin between operations, or operations may be performed in an order otherthan that shown, or operations may be adjusted so that they occur atslightly different times, or may be distributed in a system which allowsthe occurrence of the processing operations at various intervalsassociated with the processing.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, it will be apparent thatcertain changes and modifications can be practiced within the scope ofthe appended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the variousembodiments described in the present disclosure are not to be limited tothe details given herein, but may be modified within the scope andequivalents of the appended claims.

What is claimed is:
 1. A method, comprising, receiving a plurality ofactivity data streams from a plurality of devices, each activity datastream being associated with physical activity data of a user; andassembling a unified activity data stream for the user over a period oftime, the unified activity data stream including data segments from thedata streams of at least two devices of the plurality of devices, thedata segments being organized time-wise over the period of time, whereinoperations of the method are executed by a processor.
 2. The method asrecited in claim 1, wherein assembling the unified activity data streamfurther includes: detecting a first time period with data available fromtwo or more devices; identifying a priority for each of the two or moredevices; and adding to the unified activity data stream a first datasegment covering the first time period, the first data segment havingthe data from a device with a higher priority from two or more devices.3. The method as recited in claim 1, wherein assembling the unifiedactivity data stream further includes: detecting a second time periodwith data available from a single device from the plurality of devices;and adding to the unified activity data stream a second data segmentcovering the second time period, the second data segment having the datafrom the single device.
 4. The method as recited in claim 1, furtherincluding: generating display data for the unified activity data stream,the display data configured for presentation on an interactive userinterface, the interactive user interface enabling user selection of atleast one of the data segments to identify additional information forthe data segment and further identify which of the plurality of devicesproduced the physical activity data of the user for the selected datasegment.
 5. The method as recited in claim 4, further including:identifying an activity being performed by the user during at least oneof the data segments; and presenting the activity in the interactiveuser interface.
 6. The method as recited in claim 5, wherein identifyingthe activity being performed by the user is calculated based on one ormore of data in a calendar of the user, or a pattern found in the dataof one or more devices from the plurality of devices, or informationentered by the user regarding the activity.
 7. The method as recited inclaim 1, further including: sending at least part of the unifiedactivity data stream to one or more of the plurality of devices.
 8. Themethod as recited in claim 1, further including: analyzing the unifiedactivity data stream to identify a stride length of the user; andsending the stride length of the user to one or more devices from theplurality of devices.
 9. The method as recited in claim 1, furtherincluding: parsing the received plurality of data streams, beforeassembling the unified activity data stream, to identify data types andrespective data values in the data streams.
 10. A method comprising:receiving data streams regarding activity of a user, each data streambeing associated with a respective device of a plurality of devices;evaluating one or more rules for consolidating the data streams into aunified activity data stream, wherein evaluating the one or more rulesfurther includes: identifying first time segments where data isavailable from a single device from the plurality of devices; adding theavailable data to the unified activity data stream for the first timesegments; identifying second time segments where data exists from two ormore devices from the plurality of devices; and adding data to thesecond time segments based on the existing data from the two or moredevices and based on the one or more rules; and storing the unifiedactivity data stream for presentation to the user, wherein operations ofthe method are executed by a processor.
 11. The method as recited inclaim 10, wherein adding data to the second time segments furtherincludes: identifying a priority for each of the plurality of devices;and for each second time segment, selecting the data from a device witha highest priority from the plurality of devices having data for thesecond time segment.
 12. The method as recited in claim 10, whereinadding data to the second time segments further includes: for eachsecond time segment, calculating a statistical measure of the data fromdevices having data for the second time segment, wherein the statisticalmeasure is one of mean, or mode, or median, or maximum, or minimum; andadding the calculated statistical measure to the unified activity datastream for the respective second time segment.
 13. The method as recitedin claim 10, wherein adding data to the second time segments furtherincludes: for each second time segment, calculating unified data for therespective second time segment according to a voting system that selectsdata based on a grouping of the data, wherein the voting systemidentifies clusters of data points and selects a cluster with most datapoints.
 14. The method as recited in claim 10, wherein the evaluatingthe one or more rules further includes: identifying an activity beingperformed by the user, wherein at least one of the rules from the one ormore rules is based on which activity is being performed by the user.15. The method as recited in claim 10, wherein each rule includes atrigger and an action, wherein the trigger includes one or moreconditions, wherein the action is performed based on the one or moreconditions.
 16. The method as recited in claim 10, further including:sending at least part of the unified activity data stream the one ormore of the plurality of devices.
 17. The method as recited in claim 10,further including: analyzing the unified activity data stream toidentify a stride length of the user; and sending the stride length ofthe user to one or more devices from the plurality of devices.
 18. Themethod as recited in claim 10, further including: receivingenvironmental data about an environment of the user, wherein at leastone rule from the one or more rules is based on the environmental data.19. The method as recited in claim 10, wherein operations of the methodare performed by a computer program when executed by one or moreprocessors, the computer program being embedded in a non-transitorycomputer-readable storage medium.
 20. The method as recited in claim 10,further including: detecting that the user is performing a jointactivity with a friend; and adding exercise data received from a deviceassociated with the friend to the unified activity data stream.
 21. Aserver comprising: a communications module operable to receive aplurality of activity data streams from a plurality of devices, eachactivity data stream being associated with physical activity data of auser; a memory for storing the plurality of activity data streams and aunified activity data stream that includes data segments from the datastreams of at least two devices of the plurality of devices; and aprocessor operable to assemble the unified activity data stream for theuser over a period of time, the data segments being organized time-wiseover the period of time.
 22. The server as recited in claim 21, whereinthe processor generates display data for the unified activity datastream, the display data configured for presentation on an interactiveuser interface, the interactive user interface enabling user selectionof at least one of the data segments to identify additional informationfor the data segment and further identify which of the plurality ofdevices produced the physical activity data of the user for the selecteddata segment, wherein the interactive user interface includes one ormore of an activity summary, the activity summary including a number ofsteps taken daily, a number of floors climbed daily, a distance traveleddaily, or a number of calories burned daily.
 23. The server as recitedin claim 22, wherein the interactive user interface provides options tothe user for selecting which devices to include in the unified activitydata stream, and for prioritizing the devices for inclusion in theunified activity data stream.
 24. The server as recited in claim 22,wherein the interactive user interface includes an activity displayinterface including: activities detected during a day; and a graphicalrepresentation identifying activity levels throughout the day.
 25. Theserver as recited in claim 21, wherein the server is coupled to a timeseries database for storing the plurality of activity data streams andthe unified activity data stream.
 26. The server as recited in claim 21,wherein the processor assembles the unified activity data stream byutilizing one or more rules, each rule including a trigger and anaction, wherein the trigger includes one or more conditions, wherein theaction is performed based on the one or more conditions.
 27. The serveras recited in claim 21, wherein the unified activity data stream isassociated with a measurement selected from a group consisting of stepstaken, energy expenditure, vertical distance ascended or descended,heart rate, heart rate variability, heart rate recovery, location,heading, elevation, speed, distance traveled, swimming laps, bloodpressure, blood glucose, skin conduction, body temperature,electromyography, electroencephalography, weight, body fat, caloricintake, medication intake, sleep state, sleep phase, sleep quality,sleep duration, or restoration rate.
 28. A non-transitorycomputer-readable storage medium storing a computer program, thecomputer-readable storage medium comprising: program instructions forreceiving a plurality of activity data streams from a plurality ofdevices, each activity data stream being associated with physicalactivity data of a user; and program instructions for assembling aunified activity data stream for the user over a period of time, theunified activity data stream including data segments from the datastreams of at least two devices of the plurality of devices, the datasegments being organized time-wise over the period of time.
 29. Thestorage medium as recited in claim 28, wherein assembling the unifiedactivity data stream further includes: program instructions fordetecting a first time period with data available from two or moredevices; program instructions for identifying a priority for each of thetwo or more devices; program instructions for adding to the unifiedactivity data stream a first data segment covering the first timeperiod, the first data segment having the data from a device with ahighest priority from two or more devices; program instructions fordetecting a second time period with data available from a single devicefrom the plurality of devices; and program instructions for adding tothe unified activity data stream a second data segment covering thesecond time period, the second data segment having the data from thesingle device.
 30. The storage medium as recited in claim 28, furtherincluding: program instructions for generating display data for theunified activity data stream, the display data configured forpresentation on an interactive user interface, the interactive userinterface enabling user selection of at least one of the data segmentsto identify additional information for the data segment and furtheridentify which of the plurality of devices produced the physicalactivity data of the user for the selected data segment.